feat:node-modules

node_modules/mathjs/lib/esm/function/statistics/mean.js

@@ -0,0 +1,94 @@
+import { containsCollections, deepForEach, reduce } from '../../utils/collection.js';
+import { arraySize } from '../../utils/array.js';
+import { factory } from '../../utils/factory.js';
+import { improveErrorMessage } from './utils/improveErrorMessage.js';
+var name = 'mean';
+var dependencies = ['typed', 'add', 'divide'];
+export var createMean = /* #__PURE__ */factory(name, dependencies, _ref => {
+  var {
+    typed,
+    add,
+    divide
+  } = _ref;
+  /**
+   * Compute the mean value of matrix or a list with values.
+   * In case of a multidimensional array, the mean of the flattened array
+   * will be calculated. When `dim` is provided, the maximum over the selected
+   * dimension will be calculated. Parameter `dim` is zero-based.
+   *
+   * Syntax:
+   *
+   *     math.mean(a, b, c, ...)
+   *     math.mean(A)
+   *     math.mean(A, dimension)
+   *
+   * Examples:
+   *
+   *     math.mean(2, 1, 4, 3)                     // returns 2.5
+   *     math.mean([1, 2.7, 3.2, 4])               // returns 2.725
+   *
+   *     math.mean([[2, 5], [6, 3], [1, 7]], 0)    // returns [3, 5]
+   *     math.mean([[2, 5], [6, 3], [1, 7]], 1)    // returns [3.5, 4.5, 4]
+   *
+   * See also:
+   *
+   *     median, min, max, sum, prod, std, variance
+   *
+   * @param {... *} args  A single matrix or or multiple scalar values
+   * @return {*} The mean of all values
+   */
+  return typed(name, {
+    // mean([a, b, c, d, ...])
+    'Array | Matrix': _mean,
+    // mean([a, b, c, d, ...], dim)
+    'Array | Matrix, number | BigNumber': _nmeanDim,
+    // mean(a, b, c, d, ...)
+    '...': function _(args) {
+      if (containsCollections(args)) {
+        throw new TypeError('Scalar values expected in function mean');
+      }
+      return _mean(args);
+    }
+  });
+
+  /**
+   * Calculate the mean value in an n-dimensional array, returning a
+   * n-1 dimensional array
+   * @param {Array} array
+   * @param {number} dim
+   * @return {number} mean
+   * @private
+   */
+  function _nmeanDim(array, dim) {
+    try {
+      var sum = reduce(array, dim, add);
+      var s = Array.isArray(array) ? arraySize(array) : array.size();
+      return divide(sum, s[dim]);
+    } catch (err) {
+      throw improveErrorMessage(err, 'mean');
+    }
+  }
+
+  /**
+   * Recursively calculate the mean value in an n-dimensional array
+   * @param {Array} array
+   * @return {number} mean
+   * @private
+   */
+  function _mean(array) {
+    var sum;
+    var num = 0;
+    deepForEach(array, function (value) {
+      try {
+        sum = sum === undefined ? value : add(sum, value);
+        num++;
+      } catch (err) {
+        throw improveErrorMessage(err, 'mean', value);
+      }
+    });
+    if (num === 0) {
+      throw new Error('Cannot calculate the mean of an empty array');
+    }
+    return divide(sum, num);
+  }
+});